This experiment is different from the exp-01.rmd which was just a one off experiment to make sure things look good. In this experiment, 6 different gamma lot reagents were sampled from the gamma shipment and then run.
There are 3 types of standards in the plate 1. Gamma lot standards 2. Beta lot standards
3. Platinum standards
Platinum standards
Each panel has two sets of platinum standards. Here is how each set is divided
Panel 1 - Platinum Set 1 –> CXCL8, FLT3L, MUC16, IL1R2 Platinum Set 2 –> NA(not part of our 7 marker panel)
Panel 2 - Platinum Set 1 –> CEACAM5, WFDC2 Platinum Set 2 –> TNC
Import data from pate using these commands
pate-rnd xmap-collector --include_file_str GammaLot_QC \--out_file_name gamma_lot_qc.csv
##
ℹ Downloading user_data_frames/gamma_lot_qc.csv
✓ Saved user_data_frames/gamma_lot_qc.csv to /home/ddhillon/proj…
## [1] TRUE
import data locally
## New names:
## * `` -> ...1
| Name | Piped data |
| Number of rows | 18432 |
| Number of columns | 45 |
| _______________________ | |
| Column type frequency: | |
| character | 12 |
| Date | 1 |
| logical | 10 |
| numeric | 22 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| xponent_id | 0 | 1.00 | 3 | 19 | 0 | 66 | 0 |
| sample_type | 0 | 1.00 | 5 | 10 | 0 | 4 | 0 |
| well_id | 0 | 1.00 | 2 | 3 | 0 | 384 | 0 |
| well_row | 0 | 1.00 | 1 | 1 | 0 | 16 | 0 |
| assay | 0 | 1.00 | 3 | 7 | 0 | 7 | 0 |
| conc_units | 0 | 1.00 | 4 | 5 | 0 | 2 | 0 |
| instrument_serial | 5376 | 0.71 | 13 | 13 | 0 | 6 | 0 |
| computer_name | 5376 | 0.71 | 9 | 9 | 0 | 6 | 0 |
| file_name | 0 | 1.00 | 54 | 54 | 0 | 14 | 0 |
| xponent_comments | 13855 | 0.25 | 12 | 12 | 0 | 1 | 0 |
| FN_comments | 0 | 1.00 | 6 | 131 | 0 | 21 | 0 |
| panel | 0 | 1.00 | 7 | 7 | 0 | 2 | 0 |
Variable type: Date
| skim_variable | n_missing | complete_rate | min | max | median | n_unique |
|---|---|---|---|---|---|---|
| run_date | 5376 | 0.71 | 2022-03-18 | 2022-03-22 | 2022-03-18 | 2 |
Variable type: logical
| skim_variable | n_missing | complete_rate | mean | count |
|---|---|---|---|---|
| Control1_control_expected_concentration | 18432 | 0 | NaN | : |
| Control2_control_expected_concentration | 18432 | 0 | NaN | : |
| Control1_control_range_low | 18432 | 0 | NaN | : |
| Control2_control_range_low | 18432 | 0 | NaN | : |
| Control1_control_range_high | 18432 | 0 | NaN | : |
| Control2_control_range_high | 18432 | 0 | NaN | : |
| calc_conc_min_truncated | 0 | 1 | 0.20 | FAL: 14785, TRU: 3647 |
| calc_conc_max_truncated | 0 | 1 | 0.01 | FAL: 18235, TRU: 197 |
| avg_conc_min_truncated | 0 | 1 | 0.20 | FAL: 14708, TRU: 3724 |
| avg_conc_max_truncated | 0 | 1 | 0.03 | FAL: 17938, TRU: 494 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| …1 | 0 | 1.00 | 26190.91 | 15011.32 | 264.00 | 13067.75 | 26249.50 | 39166.25 | 52211.00 | ▇▇▇▇▇ |
| well_column | 0 | 1.00 | 12.50 | 6.92 | 1.00 | 6.75 | 12.50 | 18.25 | 24.00 | ▇▇▆▇▇ |
| dilution_factor | 2016 | 0.89 | 3.85 | 6.59 | 1.00 | 1.00 | 1.00 | 2.00 | 20.00 | ▇▁▁▁▁ |
| standard_expected_concentration | 16704 | 0.09 | 3963.20 | 8678.03 | 2.32 | 42.79 | 451.41 | 2738.75 | 43820.00 | ▇▁▁▁▁ |
| bead_count | 135 | 0.99 | 48.73 | 10.67 | 0.00 | 41.00 | 48.00 | 55.00 | 120.00 | ▁▇▇▁▁ |
| median_mfi | 141 | 0.99 | 4450.74 | 12523.13 | 24.00 | 59.00 | 104.00 | 1283.00 | 92603.00 | ▇▁▁▁▁ |
| avg_median_mfi | 12 | 1.00 | 4420.06 | 12472.28 | 40.58 | 59.42 | 99.00 | 1244.75 | 90050.00 | ▇▁▁▁▁ |
| pct_cv_median_mfi | 12 | 1.00 | 7.65 | 4.21 | 0.00 | 4.63 | 7.27 | 9.60 | 34.31 | ▇▇▁▁▁ |
| net_mfi | 141 | 0.99 | 4387.18 | 12523.67 | -26.58 | 1.00 | 37.58 | 1223.92 | 92551.08 | ▇▁▁▁▁ |
| avg_net_mfi | 714 | 0.96 | 4529.05 | 12686.77 | -12.17 | 0.61 | 47.54 | 1411.81 | 89998.08 | ▇▁▁▁▁ |
| calc_conc | 429 | 0.98 | 3020.72 | 11326.15 | 0.00 | 6.17 | 40.20 | 988.61 | 159139.94 | ▇▁▁▁▁ |
| avg_conc | 428 | 0.98 | 3020.56 | 11284.23 | 0.00 | 5.71 | 40.75 | 1003.78 | 155942.93 | ▇▁▁▁▁ |
| conc_pct_cv | 5754 | 0.69 | 9.21 | 12.32 | 0.00 | 2.15 | 5.08 | 11.84 | 110.87 | ▇▁▁▁▁ |
| pct_recovery | 16739 | 0.09 | 102.98 | 14.45 | 52.52 | 95.89 | 101.21 | 107.35 | 210.19 | ▁▇▁▁▁ |
| calc_conc_truncated | 429 | 0.98 | 2911.67 | 10766.89 | 1.66 | 12.66 | 34.92 | 988.61 | 159139.94 | ▇▁▁▁▁ |
| avg_conc_truncated | 5754 | 0.69 | 3801.87 | 12155.17 | 2.34 | 17.23 | 105.99 | 2418.72 | 155942.93 | ▇▁▁▁▁ |
| calc_conc_standards_min | 0 | 1.00 | 14.10 | 11.06 | 1.66 | 4.83 | 12.52 | 23.59 | 35.27 | ▇▆▁▂▃ |
| calc_conc_standards_max | 0 | 1.00 | 20381.35 | 17127.67 | 2493.60 | 8126.43 | 16156.41 | 31531.02 | 60903.58 | ▇▆▂▁▃ |
| avg_conc_standards_min | 0 | 1.00 | 18.51 | 14.30 | 2.34 | 8.14 | 15.59 | 28.62 | 47.38 | ▇▆▂▁▃ |
| avg_conc_standards_max | 0 | 1.00 | 16927.98 | 15707.44 | 1437.42 | 2346.91 | 15480.60 | 29068.28 | 46543.16 | ▇▆▁▂▃ |
| conc_pct_cv_truncated | 0 | 1.00 | 4.94 | 1.41 | 3.27 | 3.62 | 4.74 | 6.19 | 7.50 | ▇▁▂▃▁ |
| kit_lot | 5376 | 0.71 | 1655232.53 | 0.50 | 1655232.00 | 1655232.00 | 1655233.00 | 1655233.00 | 1655233.00 | ▇▁▁▁▇ |
why are there 3 plate 1 for panel 2? - DELETE UNTIL FURTHER CLARIFICATION
## # A tibble: 14 × 2
## file_name n
## <chr> <int>
## 1 20220317_GammaLot_QC_Panel1_Plate1_20220318_134253.csv 1536
## 2 20220317_GammaLot_QC_Panel1_Plate2_20220318_151455.csv 1536
## 3 20220317_GammaLot_QC_Panel2_Plate1_20220318_133136.csv 1152
## 4 20220317_GammaLot_QC_Panel2_Plate1_20220318_141840.csv 1152
## 5 20220317_GammaLot_QC_Panel2_Plate1_20220318_143348.csv 1152
## 6 20220317_GammaLot_QC_Panel2_Plate2_20220318_151818.csv 1152
## 7 20220321_GammaLot_QC_Panel1_Plate3_20220322_132316.csv 1536
## 8 20220321_GammaLot_QC_Panel1_Plate4_20220322_160459.csv 1536
## 9 20220321_GammaLot_QC_Panel2_Plate3_20220322_133423.csv 1152
## 10 20220322_GammaLot_QC_Panel2_Plate4_20220322_155307.csv 1152
## 11 20220329_GammaLot_QC_Panel1_Plate5_20220330_164222.csv 1536
## 12 20220329_GammaLot_QC_Panel2_Plate5_20220330_170254.csv 1152
## 13 20220330_GammaLot_QC_Panel1_Plate6_20220331_142218.csv 1536
## 14 20220330_GammaLot_QC_Panel2_Plate6_20220331_142301.csv 1152
## # A tibble: 4 × 2
## file_name n
## <chr> <int>
## 1 20220317_GammaLot_QC_Panel1_Plate1_20220318_134253.csv 1536
## 2 20220317_GammaLot_QC_Panel2_Plate1_20220318_133136.csv 1152
## 3 20220317_GammaLot_QC_Panel2_Plate1_20220318_141840.csv 1152
## 4 20220317_GammaLot_QC_Panel2_Plate1_20220318_143348.csv 1152
| Name | Piped data |
| Number of rows | 3456 |
| Number of columns | 45 |
| _______________________ | |
| Column type frequency: | |
| character | 11 |
| Date | 1 |
| logical | 10 |
| numeric | 22 |
| ________________________ | |
| Group variables | file_name |
Variable type: character
| skim_variable | file_name | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|---|
| xponent_id | 20220317_GammaLot_QC_Panel2_Plate1_20220318_133136.csv | 0 | 1.00 | 3 | 19 | 0 | 51 | 0 |
| xponent_id | 20220317_GammaLot_QC_Panel2_Plate1_20220318_141840.csv | 0 | 1.00 | 3 | 19 | 0 | 51 | 0 |
| xponent_id | 20220317_GammaLot_QC_Panel2_Plate1_20220318_143348.csv | 0 | 1.00 | 3 | 19 | 0 | 51 | 0 |
| sample_type | 20220317_GammaLot_QC_Panel2_Plate1_20220318_133136.csv | 0 | 1.00 | 5 | 10 | 0 | 4 | 0 |
| sample_type | 20220317_GammaLot_QC_Panel2_Plate1_20220318_141840.csv | 0 | 1.00 | 5 | 10 | 0 | 4 | 0 |
| sample_type | 20220317_GammaLot_QC_Panel2_Plate1_20220318_143348.csv | 0 | 1.00 | 5 | 10 | 0 | 4 | 0 |
| well_id | 20220317_GammaLot_QC_Panel2_Plate1_20220318_133136.csv | 0 | 1.00 | 2 | 3 | 0 | 384 | 0 |
| well_id | 20220317_GammaLot_QC_Panel2_Plate1_20220318_141840.csv | 0 | 1.00 | 2 | 3 | 0 | 384 | 0 |
| well_id | 20220317_GammaLot_QC_Panel2_Plate1_20220318_143348.csv | 0 | 1.00 | 2 | 3 | 0 | 384 | 0 |
| well_row | 20220317_GammaLot_QC_Panel2_Plate1_20220318_133136.csv | 0 | 1.00 | 1 | 1 | 0 | 16 | 0 |
| well_row | 20220317_GammaLot_QC_Panel2_Plate1_20220318_141840.csv | 0 | 1.00 | 1 | 1 | 0 | 16 | 0 |
| well_row | 20220317_GammaLot_QC_Panel2_Plate1_20220318_143348.csv | 0 | 1.00 | 1 | 1 | 0 | 16 | 0 |
| assay | 20220317_GammaLot_QC_Panel2_Plate1_20220318_133136.csv | 0 | 1.00 | 3 | 7 | 0 | 3 | 0 |
| assay | 20220317_GammaLot_QC_Panel2_Plate1_20220318_141840.csv | 0 | 1.00 | 3 | 7 | 0 | 3 | 0 |
| assay | 20220317_GammaLot_QC_Panel2_Plate1_20220318_143348.csv | 0 | 1.00 | 3 | 7 | 0 | 3 | 0 |
| conc_units | 20220317_GammaLot_QC_Panel2_Plate1_20220318_133136.csv | 0 | 1.00 | 5 | 5 | 0 | 1 | 0 |
| conc_units | 20220317_GammaLot_QC_Panel2_Plate1_20220318_141840.csv | 0 | 1.00 | 5 | 5 | 0 | 1 | 0 |
| conc_units | 20220317_GammaLot_QC_Panel2_Plate1_20220318_143348.csv | 0 | 1.00 | 5 | 5 | 0 | 1 | 0 |
| instrument_serial | 20220317_GammaLot_QC_Panel2_Plate1_20220318_133136.csv | 0 | 1.00 | 13 | 13 | 0 | 1 | 0 |
| instrument_serial | 20220317_GammaLot_QC_Panel2_Plate1_20220318_141840.csv | 0 | 1.00 | 13 | 13 | 0 | 1 | 0 |
| instrument_serial | 20220317_GammaLot_QC_Panel2_Plate1_20220318_143348.csv | 0 | 1.00 | 13 | 13 | 0 | 1 | 0 |
| computer_name | 20220317_GammaLot_QC_Panel2_Plate1_20220318_133136.csv | 0 | 1.00 | 9 | 9 | 0 | 1 | 0 |
| computer_name | 20220317_GammaLot_QC_Panel2_Plate1_20220318_141840.csv | 0 | 1.00 | 9 | 9 | 0 | 1 | 0 |
| computer_name | 20220317_GammaLot_QC_Panel2_Plate1_20220318_143348.csv | 0 | 1.00 | 9 | 9 | 0 | 1 | 0 |
| xponent_comments | 20220317_GammaLot_QC_Panel2_Plate1_20220318_133136.csv | 967 | 0.16 | 12 | 12 | 0 | 1 | 0 |
| xponent_comments | 20220317_GammaLot_QC_Panel2_Plate1_20220318_141840.csv | 959 | 0.17 | 12 | 12 | 0 | 1 | 0 |
| xponent_comments | 20220317_GammaLot_QC_Panel2_Plate1_20220318_143348.csv | 960 | 0.17 | 12 | 12 | 0 | 1 | 0 |
| FN_comments | 20220317_GammaLot_QC_Panel2_Plate1_20220318_133136.csv | 0 | 1.00 | 6 | 131 | 0 | 11 | 0 |
| FN_comments | 20220317_GammaLot_QC_Panel2_Plate1_20220318_141840.csv | 0 | 1.00 | 6 | 129 | 0 | 11 | 0 |
| FN_comments | 20220317_GammaLot_QC_Panel2_Plate1_20220318_143348.csv | 0 | 1.00 | 6 | 77 | 0 | 7 | 0 |
| panel | 20220317_GammaLot_QC_Panel2_Plate1_20220318_133136.csv | 0 | 1.00 | 7 | 7 | 0 | 1 | 0 |
| panel | 20220317_GammaLot_QC_Panel2_Plate1_20220318_141840.csv | 0 | 1.00 | 7 | 7 | 0 | 1 | 0 |
| panel | 20220317_GammaLot_QC_Panel2_Plate1_20220318_143348.csv | 0 | 1.00 | 7 | 7 | 0 | 1 | 0 |
Variable type: Date
| skim_variable | file_name | n_missing | complete_rate | min | max | median | n_unique |
|---|---|---|---|---|---|---|---|
| run_date | 20220317_GammaLot_QC_Panel2_Plate1_20220318_133136.csv | 0 | 1 | 2022-03-18 | 2022-03-18 | 2022-03-18 | 1 |
| run_date | 20220317_GammaLot_QC_Panel2_Plate1_20220318_141840.csv | 0 | 1 | 2022-03-18 | 2022-03-18 | 2022-03-18 | 1 |
| run_date | 20220317_GammaLot_QC_Panel2_Plate1_20220318_143348.csv | 0 | 1 | 2022-03-18 | 2022-03-18 | 2022-03-18 | 1 |
Variable type: logical
| skim_variable | file_name | n_missing | complete_rate | mean | count |
|---|---|---|---|---|---|
| Control1_control_expected_concentration | 20220317_GammaLot_QC_Panel2_Plate1_20220318_133136.csv | 1152 | 0 | NaN | : |
| Control1_control_expected_concentration | 20220317_GammaLot_QC_Panel2_Plate1_20220318_141840.csv | 1152 | 0 | NaN | : |
| Control1_control_expected_concentration | 20220317_GammaLot_QC_Panel2_Plate1_20220318_143348.csv | 1152 | 0 | NaN | : |
| Control2_control_expected_concentration | 20220317_GammaLot_QC_Panel2_Plate1_20220318_133136.csv | 1152 | 0 | NaN | : |
| Control2_control_expected_concentration | 20220317_GammaLot_QC_Panel2_Plate1_20220318_141840.csv | 1152 | 0 | NaN | : |
| Control2_control_expected_concentration | 20220317_GammaLot_QC_Panel2_Plate1_20220318_143348.csv | 1152 | 0 | NaN | : |
| Control1_control_range_low | 20220317_GammaLot_QC_Panel2_Plate1_20220318_133136.csv | 1152 | 0 | NaN | : |
| Control1_control_range_low | 20220317_GammaLot_QC_Panel2_Plate1_20220318_141840.csv | 1152 | 0 | NaN | : |
| Control1_control_range_low | 20220317_GammaLot_QC_Panel2_Plate1_20220318_143348.csv | 1152 | 0 | NaN | : |
| Control2_control_range_low | 20220317_GammaLot_QC_Panel2_Plate1_20220318_133136.csv | 1152 | 0 | NaN | : |
| Control2_control_range_low | 20220317_GammaLot_QC_Panel2_Plate1_20220318_141840.csv | 1152 | 0 | NaN | : |
| Control2_control_range_low | 20220317_GammaLot_QC_Panel2_Plate1_20220318_143348.csv | 1152 | 0 | NaN | : |
| Control1_control_range_high | 20220317_GammaLot_QC_Panel2_Plate1_20220318_133136.csv | 1152 | 0 | NaN | : |
| Control1_control_range_high | 20220317_GammaLot_QC_Panel2_Plate1_20220318_141840.csv | 1152 | 0 | NaN | : |
| Control1_control_range_high | 20220317_GammaLot_QC_Panel2_Plate1_20220318_143348.csv | 1152 | 0 | NaN | : |
| Control2_control_range_high | 20220317_GammaLot_QC_Panel2_Plate1_20220318_133136.csv | 1152 | 0 | NaN | : |
| Control2_control_range_high | 20220317_GammaLot_QC_Panel2_Plate1_20220318_141840.csv | 1152 | 0 | NaN | : |
| Control2_control_range_high | 20220317_GammaLot_QC_Panel2_Plate1_20220318_143348.csv | 1152 | 0 | NaN | : |
| calc_conc_min_truncated | 20220317_GammaLot_QC_Panel2_Plate1_20220318_133136.csv | 0 | 1 | 0.12 | FAL: 1019, TRU: 133 |
| calc_conc_min_truncated | 20220317_GammaLot_QC_Panel2_Plate1_20220318_141840.csv | 0 | 1 | 0.12 | FAL: 1011, TRU: 141 |
| calc_conc_min_truncated | 20220317_GammaLot_QC_Panel2_Plate1_20220318_143348.csv | 0 | 1 | 0.12 | FAL: 1011, TRU: 141 |
| calc_conc_max_truncated | 20220317_GammaLot_QC_Panel2_Plate1_20220318_133136.csv | 0 | 1 | 0.01 | FAL: 1143, TRU: 9 |
| calc_conc_max_truncated | 20220317_GammaLot_QC_Panel2_Plate1_20220318_141840.csv | 0 | 1 | 0.01 | FAL: 1143, TRU: 9 |
| calc_conc_max_truncated | 20220317_GammaLot_QC_Panel2_Plate1_20220318_143348.csv | 0 | 1 | 0.01 | FAL: 1143, TRU: 9 |
| avg_conc_min_truncated | 20220317_GammaLot_QC_Panel2_Plate1_20220318_133136.csv | 0 | 1 | 0.07 | FAL: 1072, TRU: 80 |
| avg_conc_min_truncated | 20220317_GammaLot_QC_Panel2_Plate1_20220318_141840.csv | 0 | 1 | 0.19 | FAL: 928, TRU: 224 |
| avg_conc_min_truncated | 20220317_GammaLot_QC_Panel2_Plate1_20220318_143348.csv | 0 | 1 | 0.20 | FAL: 922, TRU: 230 |
| avg_conc_max_truncated | 20220317_GammaLot_QC_Panel2_Plate1_20220318_133136.csv | 0 | 1 | 0.03 | FAL: 1118, TRU: 34 |
| avg_conc_max_truncated | 20220317_GammaLot_QC_Panel2_Plate1_20220318_141840.csv | 0 | 1 | 0.03 | FAL: 1118, TRU: 34 |
| avg_conc_max_truncated | 20220317_GammaLot_QC_Panel2_Plate1_20220318_143348.csv | 0 | 1 | 0.03 | FAL: 1118, TRU: 34 |
Variable type: numeric
| skim_variable | file_name | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|---|
| …1 | 20220317_GammaLot_QC_Panel2_Plate1_20220318_133136.csv | 0 | 1.00 | 10727.09 | 910.22 | 9216.00 | 9899.75 | 10747.50 | 11510.25 | 12275.00 | ▇▇▇▇▇ |
| …1 | 20220317_GammaLot_QC_Panel2_Plate1_20220318_141840.csv | 0 | 1.00 | 13799.09 | 910.22 | 12288.00 | 12971.75 | 13819.50 | 14582.25 | 15347.00 | ▇▇▇▇▇ |
| …1 | 20220317_GammaLot_QC_Panel2_Plate1_20220318_143348.csv | 0 | 1.00 | 16871.09 | 910.22 | 15360.00 | 16043.75 | 16891.50 | 17654.25 | 18419.00 | ▇▇▇▇▇ |
| well_column | 20220317_GammaLot_QC_Panel2_Plate1_20220318_133136.csv | 0 | 1.00 | 12.50 | 6.93 | 1.00 | 6.75 | 12.50 | 18.25 | 24.00 | ▇▇▆▇▇ |
| well_column | 20220317_GammaLot_QC_Panel2_Plate1_20220318_141840.csv | 0 | 1.00 | 12.50 | 6.93 | 1.00 | 6.75 | 12.50 | 18.25 | 24.00 | ▇▇▆▇▇ |
| well_column | 20220317_GammaLot_QC_Panel2_Plate1_20220318_143348.csv | 0 | 1.00 | 12.50 | 6.93 | 1.00 | 6.75 | 12.50 | 18.25 | 24.00 | ▇▇▆▇▇ |
| dilution_factor | 20220317_GammaLot_QC_Panel2_Plate1_20220318_133136.csv | 126 | 0.89 | 6.33 | 8.54 | 1.00 | 1.00 | 1.00 | 20.00 | 20.00 | ▇▁▁▁▃ |
| dilution_factor | 20220317_GammaLot_QC_Panel2_Plate1_20220318_141840.csv | 126 | 0.89 | 6.33 | 8.54 | 1.00 | 1.00 | 1.00 | 20.00 | 20.00 | ▇▁▁▁▃ |
| dilution_factor | 20220317_GammaLot_QC_Panel2_Plate1_20220318_143348.csv | 126 | 0.89 | 6.33 | 8.54 | 1.00 | 1.00 | 1.00 | 20.00 | 20.00 | ▇▁▁▁▃ |
| standard_expected_concentration | 20220317_GammaLot_QC_Panel2_Plate1_20220318_133136.csv | 1044 | 0.09 | 5003.22 | 10390.67 | 8.11 | 60.31 | 601.72 | 3860.00 | 43820.00 | ▇▁▁▁▁ |
| standard_expected_concentration | 20220317_GammaLot_QC_Panel2_Plate1_20220318_141840.csv | 1044 | 0.09 | 5003.22 | 10390.67 | 8.11 | 60.31 | 601.72 | 3860.00 | 43820.00 | ▇▁▁▁▁ |
| standard_expected_concentration | 20220317_GammaLot_QC_Panel2_Plate1_20220318_143348.csv | 1044 | 0.09 | 5003.22 | 10390.67 | 8.11 | 60.31 | 601.72 | 3860.00 | 43820.00 | ▇▁▁▁▁ |
| bead_count | 20220317_GammaLot_QC_Panel2_Plate1_20220318_133136.csv | 135 | 0.88 | 44.66 | 8.56 | 0.00 | 38.00 | 43.00 | 50.00 | 85.00 | ▁▁▇▂▁ |
| bead_count | 20220317_GammaLot_QC_Panel2_Plate1_20220318_141840.csv | 0 | 1.00 | 44.64 | 8.61 | 0.00 | 38.00 | 43.00 | 50.00 | 85.00 | ▁▁▇▂▁ |
| bead_count | 20220317_GammaLot_QC_Panel2_Plate1_20220318_143348.csv | 0 | 1.00 | 44.74 | 8.32 | 35.00 | 38.00 | 43.00 | 50.00 | 85.00 | ▇▅▂▁▁ |
| median_mfi | 20220317_GammaLot_QC_Panel2_Plate1_20220318_133136.csv | 138 | 0.88 | 5572.40 | 14700.80 | 33.00 | 72.00 | 191.00 | 2170.00 | 84573.00 | ▇▁▁▁▁ |
| median_mfi | 20220317_GammaLot_QC_Panel2_Plate1_20220318_141840.csv | 3 | 1.00 | 4932.00 | 13920.93 | 28.00 | 66.00 | 118.00 | 1523.00 | 84573.00 | ▇▁▁▁▁ |
| median_mfi | 20220317_GammaLot_QC_Panel2_Plate1_20220318_143348.csv | 0 | 1.00 | 4919.27 | 13905.01 | 28.00 | 66.00 | 117.25 | 1484.00 | 84573.00 | ▇▁▁▁▁ |
| avg_median_mfi | 20220317_GammaLot_QC_Panel2_Plate1_20220318_133136.csv | 12 | 0.99 | 4964.20 | 13964.66 | 43.92 | 65.71 | 114.08 | 1520.33 | 82082.25 | ▇▁▁▁▁ |
| avg_median_mfi | 20220317_GammaLot_QC_Panel2_Plate1_20220318_141840.csv | 0 | 1.00 | 4919.33 | 13898.78 | 43.92 | 64.32 | 115.38 | 1520.33 | 82082.25 | ▇▁▁▁▁ |
| avg_median_mfi | 20220317_GammaLot_QC_Panel2_Plate1_20220318_143348.csv | 0 | 1.00 | 4919.27 | 13898.80 | 43.92 | 64.28 | 115.38 | 1520.33 | 82082.25 | ▇▁▁▁▁ |
| pct_cv_median_mfi | 20220317_GammaLot_QC_Panel2_Plate1_20220318_133136.csv | 12 | 0.99 | 7.70 | 4.23 | 0.00 | 4.47 | 7.45 | 10.55 | 19.42 | ▅▇▅▃▁ |
| pct_cv_median_mfi | 20220317_GammaLot_QC_Panel2_Plate1_20220318_141840.csv | 0 | 1.00 | 8.06 | 4.35 | 0.85 | 4.47 | 8.42 | 11.83 | 19.42 | ▆▆▇▃▁ |
| pct_cv_median_mfi | 20220317_GammaLot_QC_Panel2_Plate1_20220318_143348.csv | 0 | 1.00 | 8.07 | 4.34 | 0.85 | 4.47 | 8.42 | 11.82 | 19.42 | ▆▆▇▃▁ |
| net_mfi | 20220317_GammaLot_QC_Panel2_Plate1_20220318_133136.csv | 138 | 0.88 | 5503.56 | 14702.67 | -18.50 | 3.42 | 100.92 | 2096.56 | 84506.00 | ▇▁▁▁▁ |
| net_mfi | 20220317_GammaLot_QC_Panel2_Plate1_20220318_141840.csv | 3 | 1.00 | 4863.17 | 13922.66 | -23.00 | 0.58 | 47.92 | 1451.42 | 84506.00 | ▇▁▁▁▁ |
| net_mfi | 20220317_GammaLot_QC_Panel2_Plate1_20220318_143348.csv | 0 | 1.00 | 4850.44 | 13906.73 | -26.58 | 0.58 | 46.58 | 1429.04 | 84506.00 | ▇▁▁▁▁ |
| avg_net_mfi | 20220317_GammaLot_QC_Panel2_Plate1_20220318_133136.csv | 444 | 0.61 | 7881.85 | 17049.31 | -2.62 | 70.92 | 499.50 | 3774.50 | 82015.25 | ▇▁▁▁▁ |
| avg_net_mfi | 20220317_GammaLot_QC_Panel2_Plate1_20220318_141840.csv | 18 | 0.98 | 4927.49 | 13996.94 | -6.50 | -1.32 | 59.17 | 1476.42 | 82015.25 | ▇▁▁▁▁ |
| avg_net_mfi | 20220317_GammaLot_QC_Panel2_Plate1_20220318_143348.csv | 18 | 0.98 | 4927.43 | 13996.96 | -12.17 | -1.42 | 59.17 | 1476.42 | 82015.25 | ▇▁▁▁▁ |
| calc_conc | 20220317_GammaLot_QC_Panel2_Plate1_20220318_133136.csv | 156 | 0.86 | 5811.03 | 17061.95 | 0.04 | 15.08 | 63.75 | 3988.81 | 154944.69 | ▇▁▁▁▁ |
| calc_conc | 20220317_GammaLot_QC_Panel2_Plate1_20220318_141840.csv | 21 | 0.98 | 5158.61 | 16115.24 | 0.04 | 15.08 | 42.79 | 3069.94 | 154944.69 | ▇▁▁▁▁ |
| calc_conc | 20220317_GammaLot_QC_Panel2_Plate1_20220318_143348.csv | 18 | 0.98 | 5145.03 | 16096.05 | 0.04 | 15.08 | 42.79 | 3054.20 | 154944.69 | ▇▁▁▁▁ |
| avg_conc | 20220317_GammaLot_QC_Panel2_Plate1_20220318_133136.csv | 156 | 0.86 | 5811.03 | 16992.95 | 0.04 | 15.08 | 64.56 | 4001.05 | 147276.00 | ▇▁▁▁▁ |
| avg_conc | 20220317_GammaLot_QC_Panel2_Plate1_20220318_141840.csv | 20 | 0.98 | 5154.10 | 16044.48 | 0.04 | 15.08 | 42.79 | 3065.04 | 147276.00 | ▇▁▁▁▁ |
| avg_conc | 20220317_GammaLot_QC_Panel2_Plate1_20220318_143348.csv | 18 | 0.98 | 5145.03 | 16031.76 | 0.04 | 15.08 | 42.79 | 3065.04 | 147276.00 | ▇▁▁▁▁ |
| conc_pct_cv | 20220317_GammaLot_QC_Panel2_Plate1_20220318_133136.csv | 514 | 0.55 | 7.51 | 8.95 | 0.00 | 2.53 | 4.70 | 8.12 | 57.92 | ▇▂▁▁▁ |
| conc_pct_cv | 20220317_GammaLot_QC_Panel2_Plate1_20220318_141840.csv | 358 | 0.69 | 6.13 | 8.53 | 0.00 | 0.00 | 3.99 | 6.90 | 57.92 | ▇▁▁▁▁ |
| conc_pct_cv | 20220317_GammaLot_QC_Panel2_Plate1_20220318_143348.csv | 352 | 0.69 | 6.08 | 8.52 | 0.00 | 0.00 | 3.90 | 6.80 | 57.92 | ▇▁▁▁▁ |
| pct_recovery | 20220317_GammaLot_QC_Panel2_Plate1_20220318_133136.csv | 1048 | 0.09 | 102.36 | 12.98 | 68.99 | 96.01 | 101.73 | 105.32 | 141.89 | ▁▃▇▁▁ |
| pct_recovery | 20220317_GammaLot_QC_Panel2_Plate1_20220318_141840.csv | 1048 | 0.09 | 102.36 | 12.98 | 68.99 | 96.01 | 101.73 | 105.32 | 141.89 | ▁▃▇▁▁ |
| pct_recovery | 20220317_GammaLot_QC_Panel2_Plate1_20220318_143348.csv | 1048 | 0.09 | 102.36 | 12.98 | 68.99 | 96.01 | 101.73 | 105.32 | 141.89 | ▁▃▇▁▁ |
| calc_conc_truncated | 20220317_GammaLot_QC_Panel2_Plate1_20220318_133136.csv | 156 | 0.86 | 5513.89 | 15896.96 | 5.71 | 15.08 | 61.01 | 3988.81 | 154944.69 | ▇▁▁▁▁ |
| calc_conc_truncated | 20220317_GammaLot_QC_Panel2_Plate1_20220318_141840.csv | 21 | 0.98 | 4896.98 | 15018.61 | 5.71 | 15.08 | 42.79 | 3069.94 | 154944.69 | ▇▁▁▁▁ |
| calc_conc_truncated | 20220317_GammaLot_QC_Panel2_Plate1_20220318_143348.csv | 18 | 0.98 | 4884.08 | 15000.80 | 5.71 | 15.08 | 42.79 | 3054.20 | 154944.69 | ▇▁▁▁▁ |
| avg_conc_truncated | 20220317_GammaLot_QC_Panel2_Plate1_20220318_133136.csv | 514 | 0.55 | 7843.93 | 18705.37 | 10.32 | 60.78 | 1321.37 | 7054.25 | 147276.00 | ▇▁▁▁▁ |
| avg_conc_truncated | 20220317_GammaLot_QC_Panel2_Plate1_20220318_141840.csv | 358 | 0.69 | 6366.22 | 17038.82 | 10.32 | 45.65 | 511.03 | 4809.97 | 147276.00 | ▇▁▁▁▁ |
| avg_conc_truncated | 20220317_GammaLot_QC_Panel2_Plate1_20220318_143348.csv | 352 | 0.69 | 6318.56 | 16983.59 | 10.32 | 45.65 | 511.03 | 4624.72 | 147276.00 | ▇▁▁▁▁ |
| calc_conc_standards_min | 20220317_GammaLot_QC_Panel2_Plate1_20220318_133136.csv | 0 | 1.00 | 17.21 | 11.78 | 5.71 | 5.71 | 12.52 | 33.39 | 33.39 | ▇▇▁▁▇ |
| calc_conc_standards_min | 20220317_GammaLot_QC_Panel2_Plate1_20220318_141840.csv | 0 | 1.00 | 17.21 | 11.78 | 5.71 | 5.71 | 12.52 | 33.39 | 33.39 | ▇▇▁▁▇ |
| calc_conc_standards_min | 20220317_GammaLot_QC_Panel2_Plate1_20220318_143348.csv | 0 | 1.00 | 17.21 | 11.78 | 5.71 | 5.71 | 12.52 | 33.39 | 33.39 | ▇▇▁▁▇ |
| calc_conc_standards_max | 20220317_GammaLot_QC_Panel2_Plate1_20220318_133136.csv | 0 | 1.00 | 25228.31 | 18744.17 | 8300.00 | 8300.00 | 16039.35 | 51345.58 | 51345.58 | ▇▁▁▁▃ |
| calc_conc_standards_max | 20220317_GammaLot_QC_Panel2_Plate1_20220318_141840.csv | 0 | 1.00 | 25228.31 | 18744.17 | 8300.00 | 8300.00 | 16039.35 | 51345.58 | 51345.58 | ▇▁▁▁▃ |
| calc_conc_standards_max | 20220317_GammaLot_QC_Panel2_Plate1_20220318_143348.csv | 0 | 1.00 | 25228.31 | 18744.17 | 8300.00 | 8300.00 | 16039.35 | 51345.58 | 51345.58 | ▇▁▁▁▃ |
| avg_conc_standards_min | 20220317_GammaLot_QC_Panel2_Plate1_20220318_133136.csv | 0 | 1.00 | 23.78 | 15.61 | 10.32 | 10.32 | 15.37 | 45.65 | 45.65 | ▇▁▁▁▃ |
| avg_conc_standards_min | 20220317_GammaLot_QC_Panel2_Plate1_20220318_141840.csv | 0 | 1.00 | 23.78 | 15.61 | 10.32 | 10.32 | 15.37 | 45.65 | 45.65 | ▇▁▁▁▃ |
| avg_conc_standards_min | 20220317_GammaLot_QC_Panel2_Plate1_20220318_143348.csv | 0 | 1.00 | 23.78 | 15.61 | 10.32 | 10.32 | 15.37 | 45.65 | 45.65 | ▇▁▁▁▃ |
| avg_conc_standards_max | 20220317_GammaLot_QC_Panel2_Plate1_20220318_133136.csv | 0 | 1.00 | 21081.94 | 18183.14 | 2157.55 | 2157.55 | 15480.60 | 45607.67 | 45607.67 | ▇▇▁▁▇ |
| avg_conc_standards_max | 20220317_GammaLot_QC_Panel2_Plate1_20220318_141840.csv | 0 | 1.00 | 21081.94 | 18183.14 | 2157.55 | 2157.55 | 15480.60 | 45607.67 | 45607.67 | ▇▇▁▁▇ |
| avg_conc_standards_max | 20220317_GammaLot_QC_Panel2_Plate1_20220318_143348.csv | 0 | 1.00 | 21081.94 | 18183.14 | 2157.55 | 2157.55 | 15480.60 | 45607.67 | 45607.67 | ▇▇▁▁▇ |
| conc_pct_cv_truncated | 20220317_GammaLot_QC_Panel2_Plate1_20220318_133136.csv | 0 | 1.00 | 3.42 | 0.00 | 3.42 | 3.42 | 3.42 | 3.42 | 3.42 | ▁▁▇▁▁ |
| conc_pct_cv_truncated | 20220317_GammaLot_QC_Panel2_Plate1_20220318_141840.csv | 0 | 1.00 | 3.61 | 0.00 | 3.61 | 3.61 | 3.61 | 3.61 | 3.61 | ▁▁▇▁▁ |
| conc_pct_cv_truncated | 20220317_GammaLot_QC_Panel2_Plate1_20220318_143348.csv | 0 | 1.00 | 3.62 | 0.00 | 3.62 | 3.62 | 3.62 | 3.62 | 3.62 | ▁▁▇▁▁ |
| kit_lot | 20220317_GammaLot_QC_Panel2_Plate1_20220318_133136.csv | 0 | 1.00 | 1655233.00 | 0.00 | 1655233.00 | 1655233.00 | 1655233.00 | 1655233.00 | 1655233.00 | ▁▁▇▁▁ |
| kit_lot | 20220317_GammaLot_QC_Panel2_Plate1_20220318_141840.csv | 0 | 1.00 | 1655233.00 | 0.00 | 1655233.00 | 1655233.00 | 1655233.00 | 1655233.00 | 1655233.00 | ▁▁▇▁▁ |
| kit_lot | 20220317_GammaLot_QC_Panel2_Plate1_20220318_143348.csv | 0 | 1.00 | 1655233.00 | 0.00 | 1655233.00 | 1655233.00 | 1655233.00 | 1655233.00 | 1655233.00 | ▁▁▇▁▁ |
Seems like 20220317_GammaLot_QC_Panel2_Plate1_20220318_133136.csv has some missing bead counts - on checking with Jinesh and wilson, they suggest not using this and using the latest run of these 3.
## # A tibble: 135 × 45
## ...1 xponent_id sample_type well_id well_row well_column
## <dbl> <chr> <chr> <chr> <chr> <dbl>
## 1 9311 Assay Buffer sample O4 O 4
## 2 9312 Assay Buffer sample O5 O 5
## 3 9313 Assay Buffer sample O6 O 6
## 4 9314 Assay Buffer sample O7 O 7
## 5 9315 Assay Buffer sample O8 O 8
## 6 9316 Assay Buffer sample O11 O 11
## 7 9317 Assay Buffer sample O12 O 12
## 8 9318 Assay Buffer sample O13 O 13
## 9 9319 Assay Buffer sample O14 O 14
## 10 9320 Assay Buffer sample O15 O 15
## # … with 125 more rows, and 39 more variables: assay <chr>,
## # dilution_factor <dbl>, conc_units <chr>,
## # standard_expected_concentration <dbl>, bead_count <dbl>,
## # median_mfi <dbl>, avg_median_mfi <dbl>,
## # pct_cv_median_mfi <dbl>, net_mfi <dbl>, avg_net_mfi <dbl>,
## # calc_conc <dbl>, avg_conc <dbl>, conc_pct_cv <dbl>,
## # pct_recovery <dbl>, …
Which samples failed bead count? several bead count failures for standard 6 in the platinum set - but none of these contain the standards of interest
For the curves fit using gamma lot, we will check the interpolated beta and platinum standard distribution
Since we have one observation of each beta and platinum standard per lot, we can’t run any statistical test of comparisons. Instead, we want to check if something is wildly different among the 6 lots.
We can achieve this by using the modified z-score outlier test on these 4 observations to detect if something is ‘different’.
Beta and platinum standards
Here we fit the curve using the gamma standards, and then interpolate the beta and platinum standards. We know that these standards are the same sample, simply run with a different lot of gamma reagents and standards.
Their distribution should be within the natural variation range. But since we don’t have any data on these gamma lot reagents, we don’t know how to estimate this natural variation.
Instead, another way to check if they are ‘similar’ is using the modified z score. This is used as an outlier test and from the reagent lots sampled, neither of these should be
Beta Standards
## `summarise()` has grouped output by 'xponent_id', 'file_name'.
## You can override using the `.groups` argument.
## Warning: Removed 6 rows containing missing values (geom_point).
Platinum Standards Set 1
## `summarise()` has grouped output by 'xponent_id', 'file_name'.
## You can override using the `.groups` argument.
CEA, std 5?
## `summarise()` has grouped output by 'xponent_id', 'file_name',
## 'assay'. You can override using the `.groups` argument.
## # A tibble: 6 × 7
## xponent_id file_name assay calc_conc net_mfi computer_name
## <chr> <chr> <chr> <dbl> <dbl> <chr>
## 1 PltSet1 Std5 20220317_Ga… CEAC… 65.0 243. FM3D-CL06
## 2 PltSet1 Std5 20220317_Ga… CEAC… 64.1 260. FM3D-CL03
## 3 PltSet1 Std5 20220321_Ga… CEAC… 60.9 207. FM3D-CL01
## 4 PltSet1 Std5 20220322_Ga… CEAC… 63.8 230. FM3D-CL03
## 5 PltSet1 Std5 20220329_Ga… CEAC… 65.1 237. <NA>
## 6 PltSet1 Std5 20220330_Ga… CEAC… 68.2 243. <NA>
## # … with 1 more variable: mean_bead_count <dbl>
Platinum Standards Set 2
## `summarise()` has grouped output by 'xponent_id', 'file_name'.
## You can override using the `.groups` argument.
## Warning: `gather_()` was deprecated in tidyr 1.2.0.
## Please use `gather()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.
What’s going on for TNC std 6? (plate 3 and plate 5 seem very different)
## `summarise()` has grouped output by 'xponent_id', 'file_name',
## 'assay'. You can override using the `.groups` argument.
## # A tibble: 6 × 7
## xponent_id file_name assay calc_conc net_mfi computer_name
## <chr> <chr> <chr> <dbl> <dbl> <chr>
## 1 PltSet2 Std6 20220317_Ga… TNC 21.2 51.3 FM3D-CL06
## 2 PltSet2 Std6 20220317_Ga… TNC 21.8 51.9 FM3D-CL03
## 3 PltSet2 Std6 20220321_Ga… TNC 24.9 54.6 FM3D-CL01
## 4 PltSet2 Std6 20220322_Ga… TNC 21.4 55.3 FM3D-CL03
## 5 PltSet2 Std6 20220329_Ga… TNC 18.5 50.3 <NA>
## 6 PltSet2 Std6 20220330_Ga… TNC 21.9 73.7 <NA>
## # … with 1 more variable: mean_bead_count <dbl>
Platinum Standards Set 1 and 2
## `summarise()` has grouped output by 'file_name', 'assay'. You
## can override using the `.groups` argument.
## `summarise()` has grouped output by 'assay'. You can override
## using the `.groups` argument.
## `summarise()` has grouped output by 'file_name', 'assay'. You
## can override using the `.groups` argument.
## `summarise()` has grouped output by 'assay'. You can override
## using the `.groups` argument.
Generally speaking, CVs are higher for Std 6 and Std1 for a lot of the markers, so it isn’t surprising to see some outliers in the platinum standards for S6 and S1. If there were true differences in one of the lots, we would have seen this consistently.
samples
In order for us to value assign the gamma lot standards against our platinum standards, we will
Fit a curve using the platinum standards for each of the 6 runs
Interpolate the gamma lot standards to the curve fit by platinum standards
Take the average across all 6 runs and assign that as the value of each gamma lot standard
We need to send the value assignments to RDS for ALL 20 proteins, not just the 7 markers because RDS is not aware that we are only going forward with the 20.
## New names:
## * `` -> ...1
This deck provides with the value assignments
Now we have the data we need to fit a curve using the platinum standards. The next thing we need is the gamma lot standards data which we will “infer” on
## # A tibble: 648 × 4
## xponent_id file_name assay n
## <chr> <chr> <chr> <int>
## 1 PltSet1 Std1 20220317_GammaLot_QC_Panel1_Plate1_2… AFP 6
## 2 PltSet1 Std1 20220317_GammaLot_QC_Panel1_Plate1_2… CCL20 6
## 3 PltSet1 Std1 20220317_GammaLot_QC_Panel1_Plate1_2… CXCL8 6
## 4 PltSet1 Std1 20220317_GammaLot_QC_Panel1_Plate1_2… EGF 6
## 5 PltSet1 Std1 20220317_GammaLot_QC_Panel1_Plate1_2… FLT3… 6
## 6 PltSet1 Std1 20220317_GammaLot_QC_Panel1_Plate1_2… HGF 6
## 7 PltSet1 Std1 20220317_GammaLot_QC_Panel1_Plate1_2… IL1R2 6
## 8 PltSet1 Std1 20220317_GammaLot_QC_Panel1_Plate1_2… IL6 6
## 9 PltSet1 Std1 20220317_GammaLot_QC_Panel1_Plate1_2… KLK3 6
## 10 PltSet1 Std1 20220317_GammaLot_QC_Panel1_Plate1_2… MUC16 6
## # … with 638 more rows
We also define a function that uses the average over the replicates and then fits the curve.
Fit curve and infer data
| Name | Piped data |
| Number of rows | 648 |
| Number of columns | 9 |
| _______________________ | |
| Column type frequency: | |
| character | 4 |
| list | 1 |
| numeric | 4 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| assay | 0 | 1 | 3 | 7 | 0 | 20 | 0 |
| xponent_id | 0 | 1 | 9 | 9 | 0 | 6 | 0 |
| sample_type | 0 | 1 | 6 | 6 | 0 | 1 | 0 |
| file_name | 0 | 1 | 54 | 54 | 0 | 11 | 0 |
Variable type: list
| skim_variable | n_missing | complete_rate | n_unique | min_length | max_length |
|---|---|---|---|---|---|
| units | 648 | 0 | 0 | 1 | 1 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| net_mfi | 0 | 1.00 | 14163.02 | 21739.15 | 11.75 | 255.52 | 2429.58 | 20020.27 | 107303.9 | ▇▂▁▁▁ |
| standard_expected_concentration | 0 | 1.00 | 8773.47 | 27161.76 | 0.04 | 57.97 | 504.20 | 3985.00 | 234160.0 | ▇▁▁▁▁ |
| bead_count | 0 | 1.00 | 47.72 | 9.91 | 35.00 | 40.00 | 47.00 | 54.00 | 81.0 | ▇▆▃▁▁ |
| inferred_concentration | 13 | 0.98 | 45885.47 | 273323.46 | 0.00 | 22.35 | 513.96 | 6921.13 | 3134051.2 | ▇▁▁▁▁ |
Several missing inferred_concentrations for the standard 1 in gamma lot (MIF, OPN, MUC1)
net_mfi for S1 for MUC1 gamma (and beta) is way higher than platinum standard - for the gamma lot S1 that are outside the S1 , we will assign them the S1 from the platinum standard itself since they are not in the quantiation range.
## # A tibble: 13 × 9
## assay xponent_id net_mfi sample_type standard_expected_… units
## <chr> <chr> <dbl> <chr> <dbl> <lis>
## 1 MUC1 Standard1 43368. sample 40 <dbl>
## 2 OPN Standard1 35532. sample 234160 <dbl>
## 3 MIF Standard1 40445. sample 129470 <dbl>
## 4 MIF Standard1 46497. sample 129470 <dbl>
## 5 MUC1 Standard1 37998. sample 40 <dbl>
## 6 MUC1 Standard1 42132. sample 40 <dbl>
## 7 MIF Standard1 37366. sample 129470 <dbl>
## 8 MIF Standard1 44298. sample 129470 <dbl>
## 9 CCL20 Standard1 45934. sample 6990 <dbl>
## 10 MUC1 Standard1 38321. sample 40 <dbl>
## 11 OPN Standard1 31437. sample 234160 <dbl>
## 12 MIF Standard1 39026. sample 129470 <dbl>
## 13 MIF Standard1 42978. sample 129470 <dbl>
## # … with 3 more variables: bead_count <dbl>, file_name <chr>,
## # inferred_concentration <dbl>
## Warning in self$trans$transform(x): NaNs produced
## Warning: Transformation introduced infinite values in continuous
## x-axis
## Warning: Removed 2 rows containing missing values (geom_point).
Let’s generate these plots for all gamma standards for ALL proteins
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## Warning in self$trans$transform(x): NaNs produced
## Warning: Transformation introduced infinite values in continuous
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## Warning: Removed 2 rows containing missing values (geom_point).
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For the final assignments, we will take the average over all the runs (5 for panel 1 and 6 for panel 2), and for the ones that are beyond the quantitation range of S1, we simply assign them S1 (even if they are quantifiable because we are not confident in our extrapolations).
## `summarise()` has grouped output by 'assay'. You can override
## using the `.groups` argument.
## Joining, by = "assay"
## Auto-refreshing stale OAuth token.
## x Request failed [429]. Retry 1 happens in 2.4 seconds ...
## x Request failed [429]. Retry 2 happens in 7.9 seconds ...
## x Request failed [429]. Retry 3 happens in 25.7 seconds ...
## ✓ Writing to "gamma-lot-specs-2022".
## ✓ Writing to sheet 'value_assignments'.
We need to generate some estimate of precision for the samples from gamma lot (for all markers - not just the 7 proteins)
We will do this for the clinical reference samples only that we are sending to RDS.
standard_expected_concentration to the value assignments we did in part 2.inferred_concentrations for the reference samples.Join with the gamma assignments
We will provide intra-CVs for
## `summarise()` has grouped output by 'xponent_id', 'assay'. You
## can override using the `.groups` argument.
## `summarise()` has grouped output by 'xponent_id'. You can
## override using the `.groups` argument.
## `summarise()` has grouped output by 'xponent_id', 'assay'. You
## can override using the `.groups` argument.
## `summarise()` has grouped output by 'xponent_id'. You can
## override using the `.groups` argument.
## ✓ Writing to "gamma-lot-specs-2022".
## ✓ Writing to sheet 'Reference Samples Specs'.